Parallelize score based strucutre learning

reCTBN/src/structure_learning/score_based_algorithm.rs

@@ -6,6 +6,9 @@ use crate::structure_learning::score_function::ScoreFunction;
 use crate::structure_learning::StructureLearningAlgorithm;
 use crate::{process, tools::Dataset};
 
+use rayon::iter::{IntoParallelIterator, ParallelIterator};
+use rayon::prelude::ParallelExtend;
+
 pub struct HillClimbing<S: ScoreFunction> {
     score_function: S,
     max_parent_set: Option<usize>,
@@ -36,8 +39,9 @@ impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
         let max_parent_set = self.max_parent_set.unwrap_or(net.get_number_of_nodes());
         //Reset the adj matrix
         net.initialize_adj_matrix();
+        let mut learned_parent_sets: Vec<(usize, BTreeSet<usize>)> = vec![];
         //Iterate over each node to learn their parent set.
-        for node in net.get_node_indices() {
+        learned_parent_sets.par_extend(net.get_node_indices().into_par_iter().map(|node| {
             //Initialize an empty parent set.
             let mut parent_set: BTreeSet<usize> = BTreeSet::new();
             //Compute the score for the empty parent set
@@ -76,10 +80,14 @@ impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
                     }
                 }
             }
-            //Apply the learned parent_set to the network struct.
+            (node, parent_set)
-            parent_set.iter().for_each(|p| net.add_edge(*p, node));
+        }));
+            
+        for (child_node, candidate_parent_set) in learned_parent_sets {
+            for parent_node in candidate_parent_set.iter() {
+                net.add_edge(*parent_node, child_node);
+            }
         }
-
         return net;
     }
 }

reCTBN/src/structure_learning/score_function.rs

@@ -7,7 +7,7 @@ use statrs::function::gamma;
 
 use crate::{parameter_learning, params, process, tools};
 
-pub trait ScoreFunction {
+pub trait ScoreFunction: Sync {
     fn call<T>(
         &self,
         net: &T,